Author: Daina Ross
Ross, Daina, 2020 Models that go ouch, Flinders University, College of Science and Engineering
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Knee prostheses, particularly those used in knee arthroplasties, are commonly designed using mechanical modelling methods. Despite the benefits of these methods, there are persistent issues regarding chronic post-operative pain following implantation of prosthetic devices. As one of the significant indicators for revision surgery, it is important that the occurrence of chronic post- operative pain is mitigated. The integration of mechanical and neural modelling methods is yet to be explored extensively but may offer a solution to the issue. With integrated modelling methods, potential pain or discomfort may be predicted during the design phase, allowing for modifications to alleviate predicted complications prior to the device being physically created and implanted. As a first step towards developing a complete integrated mechanical/neural model for the knee, this project focused on adapting the Hodgkin-Huxley neural model for a single Ruffini ending. However, there is a significant lack of understanding regarding fundamental behaviours of the Ruffini and limited experimental data available. As a result, there are numerous discrepancies in the literature, in particular regarding what and how the Ruffini senses. Currently, Khalsa et al. (1996) provides the most complete set of experimental data that describes Ruffini behaviour and so this data was used as a guide for adaptation of the neural model (Khalsa, Hoffman et al. 1996). The Khalsa et al. (1996) experiment was replicated using computer modelling methods. This was expected to assist in determining what the Ruffini senses and the physical properties involved in how it senses. The specimen used in the original experiment was replicated using FEBio, while the Ruffini neural response was replicated using a Matlab adaptation of the Hodgkin-Huxley model. Successful integration of the mechanical and neural models was achieved, and the models were tested extensively using three material types and eighteen neural model input stimuli. None of the material/input stimuli combinations produced results that adequately described the original results, and the ‘correct’ input stimulus for the neural model is still unclear. Given that none of the models tested were able to adequately replicate the original results, two possible conclusions can be drawn: either there are physical features or characteristics missing from the mechanical or neural model that play part in the Ruffini behaviour, or there may have been complications in the original results that make them difficult to replicate (e.g. responses are from multiple Ruffini endings, or interference from other neurons). Overall, the goals of the project were not achieved and as such there is still an abundance of future work to be completed, including further adaptations to the mechanical and neural model and physically re-performing the original experiment.
Keywords: Mechanoreceptor, Ruffini ending, neural model, mechanical model, Hodgkin-Huxley, Matlab, FEBio
Subject: Engineering thesis
Thesis type: Masters
Completed: 2020
School: College of Science and Engineering
Supervisor: Associate Professor Kenneth Pope / Professor Mark Taylor